WF-001: Model Development Workflow

DOCUMENT CONTROL

Field	Value
WF ID	WF-001
Version	1.0
Status	Active

Model Development Workflow (Vertex AI)

Document Control

Version	Date	Author	Description
1.0	2023-04-14	John Doe	Initial version

Workflow Diagram

┌────────────────┐     ┌────────────────┐     ┌────────────────┐     ┌────────────────┐     ┌────────────────┐
│  Data Ingestion │ ──► │  Data Cleaning │ ──► │ Feature Engineering │ ──► │  Model Training │ ──► │  Model Deployment │
└────────────────┘     └────────────────┘     └────────────────┘     └────────────────┘     └────────────────┘

Workflow Phases

1. Data Ingestion

Objectives:

• Identify and acquire the necessary data sources for model development.
• Establish a reliable and efficient data ingestion pipeline.

Steps:

1. Assess data requirements based on the problem statement and objectives.
2. Identify and evaluate available data sources, both internal and external.
3. Establish a secure and scalable data ingestion pipeline using Vertex AI's data ingestion capabilities.
4. Validate the data ingestion pipeline to ensure complete and accurate data transfer.

Exit Criteria:

• All necessary data sources have been identified and successfully ingested into Vertex AI's data storage.
• Data ingestion pipeline is functioning correctly and can handle future data updates.

2. Data Cleaning

Objectives:

• Cleanse and preprocess the ingested data to ensure data quality and consistency.
• Handle missing values, outliers, and other data anomalies.

Steps:

1. Analyze the ingested data to identify potential data quality issues, such as missing values, outliers, and inconsistencies.
2. Implement appropriate data cleaning and preprocessing techniques using Vertex AI's data transformation capabilities.
3. Validate the cleaned data to ensure it meets the required standards and is ready for feature engineering.

Exit Criteria:

• Data is cleaned and preprocessed, with all identified data quality issues resolved.
• Cleaned data is ready for feature engineering.

3. Feature Engineering

Objectives:

• Identify and create relevant features from the cleaned data.
• Optimize the feature set to improve model performance.

Steps:

1. Analyze the cleaned data to identify potentially relevant features for the machine learning task.
2. Create new features using Vertex AI's feature engineering capabilities, such as transformations, aggregations, and feature combinations.
3. Evaluate the feature set and select the most informative features to be used in model training.
4. Validate the feature engineering process by assessing the impact on model performance.

Exit Criteria:

• A comprehensive set of features has been engineered and selected for model training.
• Feature engineering process has been validated and approved.

4. Model Training

Objectives:

• Train and optimize the machine learning model using the engineered features.
• Evaluate the model's performance and iterate as necessary.

Steps:

1. Choose an appropriate machine learning algorithm and model architecture based on the problem and data characteristics.
2. Set up the model training pipeline using Vertex AI's training capabilities.
3. Train the model, monitoring for performance and adjusting hyperparameters as needed.
4. Evaluate the trained model's performance using appropriate metrics and validation techniques.
5. Iterate on the model design and training process as necessary to improve performance.

Exit Criteria:

• The trained model meets the target performance criteria.
• The model training process has been validated and approved.

5. Model Deployment

Objectives:

• Deploy the trained model to Vertex AI's managed serving infrastructure.
• Ensure the model is accessible and ready for production use.

Steps:

1. Package the trained model for deployment using Vertex AI's model management capabilities.
2. Deploy the model to Vertex AI's managed serving infrastructure, ensuring the necessary configurations and resources are in place.
3. Validate the deployed model's functionality and performance, including testing with sample data.
4. Establish monitoring and logging mechanisms to track the model's production usage and performance.

Exit Criteria:

• The trained model has been successfully deployed to Vertex AI's managed serving infrastructure.
• The deployed model is accessible and ready for production use.
• Monitoring and logging mechanisms are in place to track the model's performance.

Success Criteria Checklist

• [ ] All necessary data sources have been identified and ingested into Vertex AI.
• [ ] Data cleaning and preprocessing have been completed, and the data is ready for feature engineering.
• [ ] Relevant features have been engineered and selected for model training.
• [ ] The trained model meets the target performance criteria.
• [ ] The trained model has been successfully deployed to Vertex AI's managed serving infrastructure.
• [ ] Monitoring and logging mechanisms are in place to track the model's production performance.